Over the years, the role that Artificial Intelligence (AI) has played in drug discovery and medicine in general, has increased substantially and continues to evolve at a rapid rate. The process of drug design and drug screening relies to a great extent on computer based techniques scanning large libraries of compounds for evaluation purposes in the search for new drug candidates. It is these advanced techniques that allow researchers to extract hidden insights from the copious amounts of data they are presented with, not to mention saving them hundreds of hours in the lab on repetitive tasks.
Many big biopharmaceutical companies harness machine learning to power their searches for potential new drugs. Most of them have collaborated with AI systems or created their own internal programmes to aid faster drug discovery and development. AI and machine learning has the ability to help alleviate some of the traditional bottlenecks found within drug discovery, it frees up the researcher’s time and allows them to accelerate the discovery of advanced drugs even faster, with the definitive goal of improving human health.
The AI and drug discovery virtual issue brings together a selection of free articles and book chapters from a variety of Springer Nature journals and eBooks. The content takes a look at the impact that AI and machine learning are having within the field of drug discovery, everything from rapid identification of drugs to actually questioning this revolutionization. Read this collection of free content here, it is available to access until 18th May, 2021.
Caitlin Cricco is a Senior Content Marketing Manager on the Institutional Marketing team, based in the New York office. She works closely with Product, Sales, and Editorial to grow awareness of Springer Nature brands and product lines within the corporate and health market place.